Why and when do learning goal orientation and attitude decrease with aging? The role of perceived remaining time and work centrality

We conducted two studies to improve our understanding of why and when older workers are focused on learning. Based on socioemotional selectivity theory, which proposes that goal focus changes with age and the perception of time, we hypothesized and found that older workers perceive their remaining time at work as more limited than younger workers which, in turn, is associated with lower learning goal orientation and a less positive attitude toward learning and development. Furthermore, we hypothesized and found that high work centrality buffers the negative association between age and perceived remaining time, and thus the indirect negative effects of age on learning goal orientation and attitude toward learning and development (through perceived remaining time). These findings suggest that scholars and practitioners should take workers’ perceived remaining time and work centrality into account when examining or stimulating learning activities among aging workers.

Early school-based parent involvement, children’s self-regulated learning and academic achievement: An Australian longitudinal study

Parent involvement is widely accepted as being associated with children’s improved educational outcomes. However, the role of early school-based parent involvement is still being established. This study investigated the mediating role of self-regulated learning behaviors in the relationship between early school-based parent involvement and children’s academic achievement, using data from the Longitudinal Study of Australian Children (N = 2616). Family socioeconomic position, Aboriginal and Torres Strait Islander status, language background, child gender and cognitive competence, were controlled, as well home and community based parent involvement activity in order to derive a more confident interpretation of the results. Structural equation modeling analyses showed that children’s self-regulated learning behaviors fully mediated the relationships between school-based parent involvement at Grade 1 and children’s reading achievement at Grade 3. Importantly, these relationships were evident for children across all socio-economic backgrounds. Although there was no direct relationship between parent involvement at Grade 1 and numeracy achievement at Grade 3, parent involvement was indirectly associated with higher children’s numeracy achievement through children’s self-regulation of learning behaviors, though this relationship was stronger for children from middle and higher socio-economic backgrounds. Implications for policy and practice are discussed, and further research recommended.

A f/oxymoron? : Women, creativity and the suburbs

Donald Horne famously wrote, ‘Australia was born urban and quickly grew suburban’ (1964), an observation that carries a weight of assumptions about suburban living. Historically, the Australian suburbs have been regarded as places of retreat, family life and female activity, and subsequently as a place where not much of interest happens. By contrast, a city's central areas are seen as more dynamic spaces and, with recent creative city thinking and planning, as potential powerhouses of innovation and creativity. This article challenges assumptions about suburban living as passive places of retreat through an examination of women in the creative workforce who are living and working in the suburbs. It draws on historical accounts of creative suburban activity and a research project that mapped and investigated the experience of creative workers in the outer suburbs of Brisbane and Melbourne. The study finds that there is much creative work occurring in suburban localities, but this is not as unusual as might be expected.

Statistical tools for analyzing water quality data

Water quality data are often collected at different sites over time to improve water quality management. Water quality data usually exhibit the following characteristics: non-normal distribution, presence of outliers, missing values, values below detection limits (censored), and serial dependence. It is essential to apply appropriate statistical methodology when analyzing water quality data to draw valid conclusions and hence provide useful advice in water management. In this chapter, we will provide and demonstrate various statistical tools for analyzing such water quality data, and will also introduce how to use a statistical software R to analyze water quality data by various statistical methods. A dataset collected from the Susquehanna River Basin will be used to demonstrate various statistical methods provided in this chapter. The dataset can be downloaded from website http://www.srbc.net/programs/CBP/nutrientprogram.htm.